Empirical studies in scaling robot learning.
Like many works in robot learning [1]}, [2]}, [3]}, ours studies empirically whether existing methods scale to new and harder robotic problems than the ones originally studied. For example, studies such as [4]} found new evidence that existing algorithms (e.g SAC), previously only studied in simulation, indeed scaled to hard problems such as real world quadrupedal locomotion. Similarly, recent empirical studies have shown that well motivated prior ideas did not scale to more difficult robotic setups [5]}, [6]}. For example, the recent work [7]} reported the surprising finding that simply switching from RL-agent generated offline data to human-collected offline data caused most offline RL approaches to degrade substantially. Surprising empirical phenomena such as this motivate studies like ours which help assess if the claims of existing methods generalize beyond the setups for which the original papers were written.
